The reproduction number (R0) is vital in epidemiology to estimate the number of infected people and trace close contacts. R0 values vary depending on social activity and type of gathering events that induce infection transmissibility and its pathophysiology dependence. In this study, we estimated the probable outbreak size of COVID-19 clusters mathematically using a simple model that can predict the number of COVID-19 cases as a function of time. We proposed a mathematical model to estimate the R0 of COVID-19 in an outbreak occurring in both local and international clusters in light of published data. Different types of clusters (religious, wedding, and industrial activity) were selected based on reported events in different countries between February and April 2020. The highest R0 values were found in wedding party events (5), followed by religious gathering events (2.5), while the lowest value was found in the industrial cluster (2). In return, this will enable us to assess the trend of coronavirus spread by comparing the model results and observed patterns. This study provides predictive COVID-19 transmission patterns in different cluster types based on different R0 values. This model offers a contact-tracing task with the predicted number of cases, to decision-makers; this would help them in epidemiological investigations by knowing when to stop.

Medical devices used in healthcare organizations are costly, and the process of selecting these devices requires considering multiple criteria such as effectiveness and ease of use. Careful selection of these devices is daunting since it entails the evaluation of various measures. This research investigates the selection process of the same type of medical devices, especially when alternatives are available, and the organization needs to make a good selection. A Multi-Criteria Decision-Making (MCDM) framework based on the integration of the Analytical Hierarchy Process (AHP) and ELimination Et Choice Translating Reality (ELECTRE) method is developed. The framework model includes 10 criteria, which are selected based on real-life inputs from professional physicians. Seven Ultrasound machines (referred to as alternatives) are evaluated using the developed framework. A case study is conducted on the best selection practice of an Ultrasound machine in a gynecology clinic based in the Kingdom of Jordan. Results revealed that the best and worst alternatives of ultrasound machines are identified and compared with all other options.

The quality of Third-Party Logistics (3PL) services represented by delivery time decides the outcome of customer satisfaction. The result of this satisfaction judges the type of Word of Mouth (WoM) that, if positive, plays a vital role in attracting non-customers who are willing in 3PL services to join as customers.

As the COVID-19 pandemic has afflicted the globe, health systems worldwide have also been significantly affected. This pandemic has impacted many sectors, including health in the Kingdom of Jordan. Crises that put heavy pressure on the health systems shoulders include the emergency departments (ED), the most demanded hospital resources during normal conditions, and critical during crises. However, managing the health systems efficiently and achieving the best planning and allocation of their EDs resources becomes crucial to improve their capabilities to accommodate the crisiss impact. Knowing critical factors affecting the patient length of stay prediction is critical to reducing the risks of prolonged waiting and clustering inside EDs. That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. Therefore, patients length of stay in EDs across waiting time duration is categorized as (low, medium, and high) using supervised machine learning (ML) approaches. Unsupervised algorithms have been applied to classify the patients length of stay in local EDs in the Kingdom of Jordan. The Arab Medical Centre Hospital is selected as a case study to justify the performance of the proposed ML model. Data that spans a time interval of 22months, covering the period before and after COVID-19, is used to train the proposed feedforward network. The proposed model is compared with other ML approaches to justify its superiority. Also, comparative and correlation analyses are conducted on the considered attributes (inputs) to help classify the LOS and the patients length of stay in the ED. The best algorithms to be used are the trees such as the decision stump, REB tree, and Random Forest and the multilayer perceptron (with batch sizes of 50 and 0.001 learning rate) for this specific problem. Results showed better performance in terms of accuracy and easiness of implementation.

Condition-based maintenance involves activities that are conducted based on the equipment's performance. Continuous monitoring of equipment will ensure that it will be maintained according to a relevant activity plan. This paper proposes a maintenance framework to analyze the application of statistical control charts for condition-based maintenance of electrical generators. The proposed framework consists of four components that collaboratively determine a performance threshold for a given piece of electrical equipment. Based on the slow progression and dynamics of mechanical failures, Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process. The analysis is based on detecting the dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. With the help of experimental methodology, failures in the performance modes and defined modes are measured. Then empirical analysis reveals how control charts respond to failure detection. The results show that X-bar consistently demonstrates failure detection capability, while R charts sometimes fail when data deviates from normality.